{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HE4UKGXRHIKNB2AFJKSDQ5JNIB","short_pith_number":"pith:HE4UKGXR","canonical_record":{"source":{"id":"2101.02669","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-01-07T18:25:47Z","cross_cats_sorted":[],"title_canon_sha256":"30238f69ed2871dc48e3f36ab910cb55fb2240ce57f24e5201fbfe792e1c5e84","abstract_canon_sha256":"2df9976a6941644f64fbfdef52ca8e1ebf71ad59e68e286f96d05c14a32d2b19"},"schema_version":"1.0"},"canonical_sha256":"3939451af13a14d0e8054aa438752d407f50472ba19afa8bc4730ed444ce745b","source":{"kind":"arxiv","id":"2101.02669","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.02669","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"arxiv_version","alias_value":"2101.02669v2","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.02669","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_12","alias_value":"HE4UKGXRHIKN","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_16","alias_value":"HE4UKGXRHIKNB2AF","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_8","alias_value":"HE4UKGXR","created_at":"2026-07-05T09:02:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HE4UKGXRHIKNB2AFJKSDQ5JNIB","target":"record","payload":{"canonical_record":{"source":{"id":"2101.02669","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-01-07T18:25:47Z","cross_cats_sorted":[],"title_canon_sha256":"30238f69ed2871dc48e3f36ab910cb55fb2240ce57f24e5201fbfe792e1c5e84","abstract_canon_sha256":"2df9976a6941644f64fbfdef52ca8e1ebf71ad59e68e286f96d05c14a32d2b19"},"schema_version":"1.0"},"canonical_sha256":"3939451af13a14d0e8054aa438752d407f50472ba19afa8bc4730ed444ce745b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:02:52.307754Z","signature_b64":"V5aN+POqV+PgRNRJBJfKsyfcQklgWeAfp6vbxNYbtHKSCYOaFhk2rISrNKsRz2Gv6Jvq6k2cdwsnjSUwAYN7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3939451af13a14d0e8054aa438752d407f50472ba19afa8bc4730ed444ce745b","last_reissued_at":"2026-07-05T09:02:52.307393Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:02:52.307393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.02669","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:02:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fwcO6rRGMthZIpZU0YggLMOyHOnXdEb7f1EZ1U8nanr2cmUx2k/1RqkA8uc6qA1TSJEm+VtHkjTZFIJg48eQCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T16:34:01.469169Z"},"content_sha256":"3d63d9ad59d181124892c5a2783b9e0165e9643a45a0fd95f59d568662258452","schema_version":"1.0","event_id":"sha256:3d63d9ad59d181124892c5a2783b9e0165e9643a45a0fd95f59d568662258452"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HE4UKGXRHIKNB2AFJKSDQ5JNIB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Krzysztof Postek, Shimrit Shtern","submitted_at":"2021-01-07T18:25:47Z","abstract_excerpt":"Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems. For that reason, first order approaches, based on online-convex-optimization, have been proposed (Ben-Tal et al. (2015), Kilinc-Karzan and Ho-Nguyen (2018)) as alternatives for the case of large-scale problems. However, these methods are either stochastic in nature or involve a binary search for the optimal value. We propose determi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.02669","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.02669/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:02:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hWcPMYEWJ6ybdniiGDNAziBCgjwyovvB1HwEmBwmz6UdSnRzFkayUl1QtAT5CJZgaVA0ApePiAWPwQ/ABEU1BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-18T16:34:01.469549Z"},"content_sha256":"5fc049ce4282ce1366ec97ae938e41943dccc00c9dfde37c6b3ae8e647b1ca76","schema_version":"1.0","event_id":"sha256:5fc049ce4282ce1366ec97ae938e41943dccc00c9dfde37c6b3ae8e647b1ca76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/bundle.json","state_url":"https://pith.science/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-18T16:34:01Z","links":{"resolver":"https://pith.science/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB","bundle":"https://pith.science/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/bundle.json","state":"https://pith.science/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HE4UKGXRHIKNB2AFJKSDQ5JNIB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HE4UKGXRHIKNB2AFJKSDQ5JNIB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2df9976a6941644f64fbfdef52ca8e1ebf71ad59e68e286f96d05c14a32d2b19","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-01-07T18:25:47Z","title_canon_sha256":"30238f69ed2871dc48e3f36ab910cb55fb2240ce57f24e5201fbfe792e1c5e84"},"schema_version":"1.0","source":{"id":"2101.02669","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.02669","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"arxiv_version","alias_value":"2101.02669v2","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.02669","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_12","alias_value":"HE4UKGXRHIKN","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_16","alias_value":"HE4UKGXRHIKNB2AF","created_at":"2026-07-05T09:02:52Z"},{"alias_kind":"pith_short_8","alias_value":"HE4UKGXR","created_at":"2026-07-05T09:02:52Z"}],"graph_snapshots":[{"event_id":"sha256:5fc049ce4282ce1366ec97ae938e41943dccc00c9dfde37c6b3ae8e647b1ca76","target":"graph","created_at":"2026-07-05T09:02:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2101.02669/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Robust optimization (RO) is one of the key paradigms for solving optimization problems affected by uncertainty. Two principal approaches for RO, the robust counterpart method and the adversarial approach, potentially lead to excessively large optimization problems. For that reason, first order approaches, based on online-convex-optimization, have been proposed (Ben-Tal et al. (2015), Kilinc-Karzan and Ho-Nguyen (2018)) as alternatives for the case of large-scale problems. However, these methods are either stochastic in nature or involve a binary search for the optimal value. We propose determi","authors_text":"Krzysztof Postek, Shimrit Shtern","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-01-07T18:25:47Z","title":"First-order algorithms for robust optimization problems via convex-concave saddle-point Lagrangian reformulation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.02669","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3d63d9ad59d181124892c5a2783b9e0165e9643a45a0fd95f59d568662258452","target":"record","created_at":"2026-07-05T09:02:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2df9976a6941644f64fbfdef52ca8e1ebf71ad59e68e286f96d05c14a32d2b19","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2021-01-07T18:25:47Z","title_canon_sha256":"30238f69ed2871dc48e3f36ab910cb55fb2240ce57f24e5201fbfe792e1c5e84"},"schema_version":"1.0","source":{"id":"2101.02669","kind":"arxiv","version":2}},"canonical_sha256":"3939451af13a14d0e8054aa438752d407f50472ba19afa8bc4730ed444ce745b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3939451af13a14d0e8054aa438752d407f50472ba19afa8bc4730ed444ce745b","first_computed_at":"2026-07-05T09:02:52.307393Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:02:52.307393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V5aN+POqV+PgRNRJBJfKsyfcQklgWeAfp6vbxNYbtHKSCYOaFhk2rISrNKsRz2Gv6Jvq6k2cdwsnjSUwAYN7DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:02:52.307754Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.02669","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d63d9ad59d181124892c5a2783b9e0165e9643a45a0fd95f59d568662258452","sha256:5fc049ce4282ce1366ec97ae938e41943dccc00c9dfde37c6b3ae8e647b1ca76"],"state_sha256":"d29afc27889b158d03119c470842ece8e34ad1481131ce685e23162d4703c31b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ouYoxHl0y/Jhv4TpvMqKCXE/Y6S67qdmAd9MTmyaj80HBHgopViEGjYImChfOf/3vkH80dLt2EN0tEvWcxpEAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-18T16:34:01.471596Z","bundle_sha256":"8d244cd01e9b7184a5ec3b5ed9cf2c73df7f9d3deaf7c9f4c522bb1a8103c71e"}}